Chris Hoyd 0:08

Welcome to Product in Healthtech, a community of product leaders innovating in healthcare. I'm Chris Hoyd, principal at Vynyl. Today we're diving into the world of healthcare data analytics with Jaya Pokuri and Vinay Nagaraj, co-founders of Bonfire Analytics, these longtime friends turned business partners are on the mission to transform how health tech companies build and execute their go to market strategies. In our conversation, we explore their journey from building a healthcare contact database to becoming an analytics platform as they navigated the winding road to product market fit, how they've helped a prosthetics company achieve sales team parity and a 3x increase in efficiency by targeting the right providers, why volume driven targeting is often counterintuitive, and how Bonfire's data reveals surprising ideal customer profiles for different health tech companies, and how they're preparing to navigate healthcare policy uncertainties while turning potential headwinds into opportunities. Let's jump into the conversation. Hey guys, thanks again for joining today. Let's just start with some background. Let's dive right in. How did you guys get into this space and what inspired you to start Bonfire?

Jaya Pokuri 1:19

So we're both co founders of Bonfire. We've known each other for a long time. We've been friends since college, and we got into the space of building Bonfire through, like, a shared vision that we both had. It comes from a bit of my background as in data science and Vinay's background in health tech sales and growth, so we both studied engineering together, but then after graduating, I moved into the data science world. Worked at a few different startups, doing a lot of work in like machine learning, different working with different data sets. And Vinay worked at a health tech -- a digital health company, and it was a bit of like some side projects that I was doing around connecting disparate data sources that oftentimes data scientists like a lot of great, publicly available data that is really useful for data science projects, but data scientists don't leverage that much. And then Vinay feeling a lot of the pain points and selling into healthcare, where we found a combination, like ways to leverage what I was building for the pain points he was feeling. Yeah, Vinay any thoughts on that?

Vinay Nagaraj 2:36

I think that was a good, good summary. Yeah, we've definitely both had exposure to, like, the startup world as well. I think had some of that DNA, having worked at startups and, you know, and so I think we were both kind of hungry for the next idea. And, you know, when we kind of joined forces, the only other thing I'll highlight is, I think we it was nice to have a shared vision from the get go. I think both of us were interested in healthcare. Both of us had a similar zest for, like, how data can be a really integral part of, like, a product offering. So yeah. And then since then, we've just kind of iterated on, like Jaya mentioned, the pain point that we're solving for as we've built Bonfire.

Chris Hoyd 3:13

That's a nice segue. Can you talk a little bit more about that pain point that you're solving for and how you guys uniquely address it?

Vinay Nagaraj 3:20

Yeah, yeah, I can, I can take that one. And so in terms of the pain point, you know, one of the things like, you know, Jaya mentioned as part of, as part of my background, is I was on the growth side, selling into healthcare. So I was at a digital health company prior to Bonfire, called Round Trip Health, which is a patient transportation platform for hospitals and health systems. And as I was going through that sales process, I realized selling it to healthcare is just incredibly difficult. A lot of companies struggle with it, and a lot of my peers who are on the commercial teams had very similar pain points. Is we don't know where to start, we don't know how big our market is, we don't know who to prospect. We don't know how to build the right business cases for our prospects. So a lot of those things came up over and over again, and I think that's where we realized that data and insights can be a really powerful way to allow these commercial and sales teams to just be very targeted and not have to resort to what, you know, we call the 'spray and pray', which is, you know, you send 1000 emails get 10 responses, one meeting. And that's what I did for a while when I was selling to, you know, doctors, physician groups, hospitals, health systems. And so that's what we're trying to eliminate with data, is just having the data and the insights from the get go, allow teams to just be a lot more, you know, efficient and effective.

Chris Hoyd 4:35

You know, I know that AI is a big part of how you guys, you know, interpret the data for the customer. Can you talk a little bit about how AI sort of fits into your product and what that does for your users?

Jaya Pokuri 4:50

I feel like there's a lot of buzz around AI right now, and it's sometimes hard to understand what, what it encompasses like it seems to like touch a lot of different things. A lot of what we do is leveraging a lot of machine learning techniques to build a lot of these insights. We try to really provide actionable insights to these health tech companies. And we'll build these insights using like machine learning techniques. We also use different AI tools to help accelerate these processes. But, yeah, I think there's just generally, there's a lot going on with AI now, and there's probably more that we can do and we are planning, but I would say right now a lot of it is really focused on ml, and then also leveraging a lot of the tools that are existing to help accelerate our like product development.

Chris Hoyd 5:47

Can you maybe talk us through a recent, you know, I understand you might not be able to divulge all the details, but a recent sort of case study of how you've helped one of your customers leverage your platform?

Vinay Nagaraj 6:00

Yeah, I'm happy to share some detail there. We actually did share a customer story recently, and so it's, you know, good timing. We can talk about them. So we work with a company called Point Designs. They're sort of a mid market prosthetic device company targeting prosthetists, orthotists, you know. And they have devices that help, essentially, amputees, and so when we started working with them, initially, you know, their pain point was they weren't really sure about their market. These patient populations who need the kind of device that they've built or the devices they've built. Where are they? Who are the prosthetists and orthotists who are serving those patients, and how can they reach them more effectively? And it was a little bit more of like the casting a wide net and see what comes through, sort of approach. And yeah, since we've been able to work with them, and you know, they've been a really great partner to work with, and have also provided feedback as they've used our data on our product, it's been really cool to see that the data has helped them in terms of identifying, you know, where are these patient populations, targeting these prosthetists and orthotists. And then ultimately, what we saw was a 3x increase in sales efficiency, which is the sort of resources they've dedicated to sales and marketing. They've been able to 3x that in terms of the return they've gotten, which is, which has been really exciting. And another thing that they also mentioned was their sales team has a lot of parity now, which is also an interesting thing, right? Is being on the sales side, parity is something that's tough for sales leaders to get to, because, you know, you don't want to, you want to sort of assign and territories and regions to reps in an equitable way, but it's hard to do that without the right data, and so that's something they shared, is now they're a point where different reps occupied this kind of spot at various times in the year. So yeah, and just generally, we've looking forward to growing with them, and they actually gave us a great kind of insight in how they've used their platform, which might be another topic we talk about, is, you know, they see us as the windshield and the CRM as the rear view mirror, which I thought was a great kind of summary of our positioning is, you know, we want to help them with getting to their market more quickly. The CRM helps, obviously, track a lot of that and make sure their sales processes are optimized. That's an example of how we help, you know, a medical device company, but we also work with other digital health companies as well.

Chris Hoyd 8:30

I also read your recent post about GLP-1 market strategies, which I thought was fascinating, both, you know, for the healthcare insights and for how it kind of showcases your guys' approach to data analysis, if you don't mind, I'd like to dive into that for a minute here. I think the article demonstrates you know how you use provider-level pharmacy claims data to generate strategic insights, right? So can you talk a little bit about your unique approach to acquiring, processing and analyzing that type of healthcare data?

Jaya Pokuri 9:03

In that article, it was a lot of work with like prescription claims data. We also work with a lot of other types of data, like medical claims data, SDOH data, which is and more like population level data. So it's a lot of different data sets from a lot of different different sources, and so there can be a lot of challenges when working with data from these different sources, because oftentimes we're trying to create insights at a specific level of granularity that isn't necessarily - we may directly get from any one source, and so we have to, like, abstract it to the level of granularity we're trying to develop insights on. For example, the prescription claims data, it's all at the patient level. And then there might be other other data sets, like SDOH data, social determinants of health data, which might be at the population level, like at a zip code level, county level, and we have to figure out how to connect these together at a different level of aggregation. In the case of the article you're talking about, it was at the health system level, while taking into account like different biases and and figure out figuring out complicated relationships in the data. For example, a really complicated relationship to figure out from that's not easy to figure out from data is like affiliations data understanding for given health systems. What are all the like the entities that are encompassed by that health system is constantly changing, like every I feel like every month we hear about different like acquisitions, roles, roll ups between different health systems. Sometimes we don't know which clinic is owned by a health system or whether it's independent, and that data is not directly like in the prescription claims data or like some and there's no data set that just like tells you. And so a lot of the that work that you were talking about and and like getting to that health system level data, as we did for the GLP article, of showing transit the health system data comes from, like connecting all these different data sources and trying to predict things that just go beyond even the the prescription, like the prescription trends. Even before doing that, we have to try to predict - okay, what are all the organizations which are part of that health system and that in itself can be a pretty complicated process, yeah, does that answer at least some of some of the question?

Chris Hoyd 11:47

Yeah, that was great. Thank you. Okay, so you just, you know, described some of the deepest complexity, I think, of the American Health System, right? Like disparate data sources, difficult to discern interrelationships between them, even more difficult to like turn, you know, the data or the relationship into an insight. Can you talk a little bit about how Bonfire is, you know, uniquely differentiated to to get to that insight is that the access to data that most others don't have, is the way that you analyze it? Is it the way that you get to the insight? Can you talk a little bit about that?

Jaya Pokuri 12:27

This is a good question. I think it's a combination of the different items that you mentioned. One obvious barrier is access to data - like a lot of the data is not publicly available, is you'd have to, like, purchase it from different sources, like, whether it's a CMS or from clearing houses, and that data can be quite expensive, but then that only gets you so far. Then there's also a lot of, like, publicly available data, which may or may not be easily accessible, like it lives a lot of the relevant public data lives across a different variety of of like sources, like it might be the CMS has a lot of lot of great data sets. Other organizations have a lot of great data sets. Sometimes it's like trying to scrape certain websites. It's not in a necessarily, like a clean, extractable format. And so one, I think, just one aspect of it is being able to beyond, like, first of all, being able to purchase data. But one aspect of it is knowing where to pull data, like what data is valuable to, like, help achieve a certain like, insight that we're trying to build, and then where to find that data, how to pull that data off these processes take a lot of domain knowledge, and it's not, definitely not something we've always had, like, there's been a lot of trials that we've had to go through of, you know, talking to, talking to experts in the field, ideating with, with mentors and stuff. But I think, like we've learned a lot about the different data sets that are available and how they all interact with each other, what the pros and cons are, and that's led to, like, I was able to, like, build towards a lot of these insights that a lot of, like a lot of companies, don't have access to.

Vinay Nagaraj 14:33

It's a great summary of, like, how data itself is super powerful. But interestingly enough, what we've also found is data alone is also not enough, right? Which is why, when we talk about insights, that's like important and also not just insights, what kind of insights? And that's where we talk about the application of the data. Because with the data, we could actually go in probably several different directions just with the foundational data. But I think what we've seen is. Again, the pain point for commercial teams is like especially strong, and I think our positioning is those commercially actionable insights are kind of our focus and our bread and butter, like business questions that come up over and over again. You know, our what's our market size? Who should we prioritize when we do outreach? How should we allocate our sales and marketing resources? How do we tell a story for ROI to a physician group? Right? A lot of these questions are answered through those insights, and I think that's where we've been able to get to that insight faster, rather than give a sales team a lot of data to say, hey, now go forth and, you know, manipulate the data yourself, right? So I just wanted to add the application of the data towards these kinds of insights, is where we've positioned ourselves.

Chris Hoyd 15:47

I love that. And I love the piece which I, you know, I guess we'll link to it when we, when we post this. But it was a cool article because it took kind of a consultative approach, right? It was like, here are some strategic frameworks alongside data insights, not for any specific customer, but it's kind of like, you know, here's the level of insight that we can drive, you know, some of it's counterintuitive. It was, you know, kind of almost a thought leadership piece, right? So I'm curious, can we expect more of that? Is that part of your your growth plan?

Jaya Pokuri 16:25

Yeah, yeah. Like, we definitely want to do more thought leadership pieces. That's something that we've been talking about a lot, and I think it's helpful for people to who read those posts, because I think at the core of that, that GLP-1 post is, is showing like, how like, based on different ICPs that different companies might have, the healthcare organizations that they should be targeting, can very different. In the market right now, it's very volume driven. Like, no matter what, like, whatever a health tech company is building, they try to sell into the organizations or the providers that have the most volume. But that's probably counter intuitive to what we try to tell is that there's more to it than that based on what you're building. There's a lot of other factors that are not necessarily related to volume, that that should be taken into account. And so hopefully we can write more pieces like that for different different segments.

Vinay Nagaraj 17:34

There's like, you know, companies that either earlier stage, and then there are companies that are more mature. So you know what, Jaya had talked about, the ICP, that's ideal customer profile. And so a lot of companies may not know what their ideal customer profile is up to a certain point. And so if we were to share more, you know, pieces like this, it might give them a better sense of like - oh, yeah. How do I build my ICP in a more thoughtful way? And then for companies who've already had a lot of success and proven out their ICP now it's like, how can data help me maximize that ICP faster, right?

Chris Hoyd 18:10

That's a beautiful way to capture it, all of that. Okay, cool, yeah. And it's back to the windshield analogy, right? Like, looking to the future for Bonfire, what do you guys think is next, sort of, in the, you know, near to intermediate term. And what's the long term vision?

Vinay Nagaraj 18:26

Near term, you know, we really feel like the portion of the the market that we want to serve. And, you know, we've seen that the pain point is, again, really strong is, you know, with digital health and medical device companies, and there's, you know, certainly think that need for the commercially actionable insights. So I think for us, near term success is, how can we find more partners to build with, to help them, obviously, build more data driven go to market and establish kind of more proof points for a lot of these metrics that we look at, you know, in terms of sales, efficiency, qualified leads, business cases, I think we want to continue doing that in the near term. I would say long term, our mission really is to accelerate Health Tech adoption through, you know, these analytics and AI driven insights, right? So we want to help those companies get their products to market faster. So get them in the hands of patients, providers, payers, faster than ever before. So long term success would be like, we're the go to kind of commercial insights platform for health tech.

Chris Hoyd 19:31

Covered some decent ground there. Do you guys have an ICP?

Vinay Nagaraj 19:36

Yeah, I think it would be. You know, if we're not we don't know our ACP, then we shouldn't necessarily be prescribing other people to find theirs. I think, yeah, we, we've done a little bit of like, I think just evaluation of where have we seen a lot of pull, and also, where can we immediately add value? I think, you know, those things. And I would say there's, there's probably three buckets - within digital health, there's two -- so there's regular SaaS digital health. So these are software tools that are being sold into physicians, physician offices, clinics, hospitals, et cetera. And we can help them just kind of go to market, find their top prospects and kind of that entire funnel of insights that we talked about. Then there's med device, which is, you know, also in a similar vein of digital health SaaS, they're selling to providers and oftentimes for device companies, there are specific codes that are very relevant, whether they're, you know, diagnosis codes or procedural codes that you know, a physician might be billing directly under. You know, we heard remote patient monitoring a lot. There are a lot of devices that fall under that fall under that bucket. So we can help drive those insights from a claim standpoint, and, you know, helping them build that commercial strategy, etc. The third bucket is going back to digital health. There's tech-enabled services, which is pretty interesting. So you know, a lot of these platforms that are aiming to connect patients to providers faster, and so they do actually provide care. But you know, technology is kind of the intermediary. How we're serving them is not necessarily to enable them to sell to providers and payers faster. It's actually to help them optimize for patient acquisition. And so what that means is they're looking for ways to get patients referred into them, right? And we have insights around patient referral patterns. So where are patients going to and from? They show up at a PCP and then go to some specialist. What does that patient journey look like? And we're seeing a lot of that use case come up too. And so we can help those companies by understanding who are the high volume refers that they should be targeting. And then also, separately, you know, we do have payer level data too, and so if they have a contract with the payer. Now, the question is, how do you activate that payer relationship? Who are the providers under that payer who might be seeing patients that might be, you know, ones that they can serve? And so a lot of those insights, you know, we have that can help those companies too. So yeah, summing up, within digital health: there's digital health SaaS, there's digital health tech enabled services, and then there's med device, and then we're playing in the kind of SMB mid market for those segments.

Chris Hoyd 22:10

You know, maybe from the product side or the technical side, what's on the, if you can talk about it, or whatever you're willing to share. What's on the, you know, sort of near term roadmap there. What are you excited to be shipping soon?

Jaya Pokuri 22:23

Yeah, absolutely. Yeah, that was a good summary of our of our ICP there. And there's a lot that we we want to be building, and a lot of things that we are currently building. One thing that we've been thinking a lot about is integrating into existing workflows, because right now we have a Bonfire platform. We have this web app where users can come in get access to a lot of the data, and sites interact with the data in different formats, like visually, seeing data on a map, etc. But it's another login, right as like, we have like, you know, users like it. They can access the data, but ideally, all the information that they need is in one place for them. And a lot of times, you know, sales people, they live in their CRMs spend a lot of time in their CRMs. And so we're thinking about a lot of those types of integrations, and it's something we're working on, on incorporating and building out right now actually, is more of these, like CRM integrations, for a lot like many aspects of our data, that it can be directly - a lot of the relevant information is directly pushed and integrated into the workflows and CRMs that our users are currently leveraging. So that's one exciting thing, I think, that we hope will more seamlessly incorporate our data into existing workflows.

Chris Hoyd 23:58

So I'm curious now this is that, you know, push back on this if you disagree, but in my tenure in health tech, this feels like a particularly, let's say, volatile time which way the federal government might go on different issues with the way AI is evolving, you know, both as a new tech paradigm that can, you know, drive new startups and innovation and new businesses, but can also be leveraged within incumbents in exciting new ways. Everything's changing right? So I'm curious if you guys, could, you know, describe what you think are the sort of headwinds right now? What are you sort of working against what's working against you? What are the tailwinds? What do you think might serve to kind of help Bonfire over the coming years?

Vinay Nagaraj 24:47

I think we don't know what data will be accessible, and to what extent, at least my, you know, sort of optimistic line of thinking is even if certain public data access gets taken. In a way that it'll come back in a different form, maybe behind a paywall. So because that data has a lot of intrinsic values, so I would hope that, if the data is going to go away, that at least, you know, having some more budget to spend can at least unlock that. I agree that is definitely a headwind, I think, separately, taking a step back even like macro economically now, I think we're obviously beyond the age of like, just limitless SaaS spend right from for companies, and I think that's affected how companies have raised. It's affected how companies operate. And so that creates both a challenge and an opportunity for us. The challenge obviously, being budgets are just harder to unlock. But at the same time, the opportunity is, you know, if we are able to build something that's super defensible and has, you know, an ROI, then it's going to be more sticky. So I think that's probably something we're looking at. And I think the tailwind there, you know, in contrast to having companies operate leaner is, you know, if they're not going to scale up a sales team like they once did, how can they still increase their revenue? And I think the answer that we're betting on is better data and better insights. So I think the data liquidity that you know, we've been able to see and be able to incorporate, and also show that having the data can make teams operate leaner and be still be effective. I think we want to continue to ride that tail end, at least.

Jaya Pokuri 26:35

It's unfortunate, but some public data sets are already being taken down, like I know there's some, there's some public data sets around, like racial diversity and like these, like social, like, more socio economic, like things related to race that have been taken down. And a lot of these are really impactful for especially, like a lot of researchers use. And so we've already started seeing some of these public data sets getting taken down, and hopefully it doesn't expand like too much from there.

Vinay Nagaraj 27:09

Data alone is, you know, the the access is, is a headwind, but I think how the healthcare system and the policy landscape is going to change, that's a huge question mark right now too. You know, I think we, there are some talk about the coding system that we use right now, the ICD 10 and CPT, and there might be reform there that also directly has implications for us, and obviously every provider and payer too. So I think those are things that we'll just have to navigate, you know, and hopefully we can help companies think through that, you know, if there are any changes.

Chris Hoyd 27:41

Okay cool. Thanks, guys, thanks for exploring that with me. I want to talk a little bit about your guys journey as founders of of Bonfire. So I suppose rather from you know, rather than looking forward and sort of speculating about what might come, I think the the journey of starting a company trying to scale it is certainly something, you know, I admire. We at Vynyl work with startup founders pretty often. Many of the leadership at Vynyl have founded companies in the past. It's kind of in our DNA, and we just love to work with entrepreneurial folks. So, you know, I think Paul Graham once said, founding a startup is like getting punched in the face repeatedly. Can you guys talk a little bit about that? You know, I think you guys started this in 2022 is that right?

Jaya Pokuri 28:33

Yeah, we found it at end of 2022 but we were like, kind of ideating on it and and even, like, trying to, like, do, like, proof of concepts even before that. I think it's like that journey to product market fit can be kind of a, as I'm sure you know, Chris, can be kind of a windy road. And even, like, I'm not even sure we're completely know if we're there. Yet it feels like, it definitely feels like we're getting closer and closer, but it's hard to know when we're actually there. When we started Bonfire, we were addressing the a lot of the same pain points we are now, but our solution was actually kind of different, and so it took a little bit of time to get to where we are now, which was more of this leveraging the data, like a lot of those claims data, social determinants of health data that we are now to to help with, like help sales teams with their go to market strategy on the analytical side. We were actually starting trying to think more about contact information, which we don't do at all now. We were thinking about, okay how can we compile and maintain the best healthcare contact information database? And that's what we spent, like, the first few months to, like, some amount of. Time when we were working on Bonfire, trying to trying to address and we had some really early customers. They didn't pay a lot, but that like we were trying to build this out for. But then that was like a, I think, like a challenging space. It didn't seem like there was as much need. There a lot of flip companies we were working with, especially the later stage, beyond the startups, they were, they were like, oh, we can figure out contact information on our own, or contact information, like, it doesn't really help as much. We can meet them in person or find them on LinkedIn, or, like, figure out other ways to get in touch with them. It was more the startups that were interested in contact information. And so after, like, working on that for a while, first, we started pivoting more to the analytics side, because it would help us, like, target these larger companies, like maybe mid-market test SMB to mid-market to even more enterprise, rather than focusing on the startups where there was a more need for insights, for their go to market strategy, rather than just having the contact information. And so that was like, one of the early shifts and trying to figure out what we're building. That we did was we just one shifted away from like, trying to build this, like healthcare contact database, to more analytics and insights, and then also trying not to focus too much on those early stage startups, and going a little bit more off market to mid market and SMB, and that's what, where we've been building, now, I'll let Vinay share some thoughts too.

Vinay Nagaraj 31:46

The Product Market Fit journey is certainly very, I think, circuitous, and it's tough to know when you're there, but you all you can do is kind of lean on signals where you are and iterate based on feedback. I think, going back to that Paul Graham quote, that's, that's a funny one, and I think a lot of founders can relate to that is, I think at the early stage, you just don't know what you don't know, right? Like, and I think that's part of why we went through an accelerator, you know, as first time founders, you know, having some level of structure mentorship, you know, and sort of processes that have been tried and tested in the past were really helpful. I think we would have, you know, without that kind of initial support, could have wasted a lot more time, you know, building the wrong thing for the wrong type of segment. I think we were able to, at least, you know, use some of those like frameworks to think more strategically about who is our customer and based on feedback. How do we get to that point sooner. So that's something, you know, to just highlight as well. And I think, generally, yeah, it's when you're a small team. I think just, there's a lot to do all the time. So you never feel like you're completely like, I think through the entire to do list, there's always more. And so it's a matter of, like, I think knowing it's a marathon and not a sprint, and being able to prioritize really effectively. So I think just that's something at least I felt, you know, and still feel, is just important to know and to be in, like, a tight feedback loop with the team on what to focus on and kind of go from there. So yeah, but it's been, I think, for both of us, been very exciting. I think we we've learned a lot, and continue to be very excited about what we're building, and, you know, the value that we've been able to introduce.

Chris Hoyd 33:31

Yeah. It seems like an incredibly cool product. You guys seem like you're anticipating, you know, the the market movements and the tech paradigms really well, and I think you've just shared some wise words for any you know future health tech founders that might be listening. So thanks, guys love the conversation. Really appreciate you joining Product in Healthtech today.

Vinay Nagaraj 33:54

Yeah, thanks for having us on Chris,

Jaya Pokuri 33:55

Thanks, Chris.

Chris Hoyd 33:58

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